Advertisement
| computer science theory columbia: The Engine of Complexity John E. Mayfield, 2013-07-09 The concepts of evolution and complexity theory have become part of the intellectual ether permeating the life sciences, the social and behavioral sciences, and, more recently, management science and economics. In this book, John E. Mayfield elegantly synthesizes core concepts from multiple disciplines to offer a new approach to understanding how evolution works and how complex organisms, structures, organizations, and social orders can and do arise based on information theory and computational science. Intended for the intellectually adventuresome, this book challenges and rewards readers with a nuanced understanding of evolution and complexity that offers consistent, durable, and coherent explanations for major aspects of our life experiences. Numerous examples throughout the book illustrate evolution and complexity formation in action and highlight the core function of computation lying at the work's heart. |
| computer science theory columbia: Twenty Lectures on Algorithmic Game Theory Tim Roughgarden, 2016-08-30 Computer science and economics have engaged in a lively interaction over the past fifteen years, resulting in the new field of algorithmic game theory. Many problems that are central to modern computer science, ranging from resource allocation in large networks to online advertising, involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, and concepts from computer science are increasingly important in economics. This book grew out of the author's Stanford University course on algorithmic game theory, and aims to give students and other newcomers a quick and accessible introduction to many of the most important concepts in the field. The book also includes case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management. |
| computer science theory columbia: The Ascent of Information Caleb Scharf, 2022-06-14 “Full of fascinating insights drawn from an impressive range of disciplines, The Ascent of Information casts the familiar and the foreign in a dramatic new light.” —Brian Greene, author of The Elegant Universe Your information has a life of its own, and it’s using you to get what it wants. One of the most peculiar and possibly unique features of humans is the vast amount of information we carry outside our biological selves. But in our rush to build the infrastructure for the 20 quintillion bits we create every day, we’ve failed to ask exactly why we’re expending ever-increasing amounts of energy, resources, and human effort to maintain all this data. Drawing on deep ideas and frontier thinking in evolutionary biology, computer science, information theory, and astrobiology, Caleb Scharf argues that information is, in a very real sense, alive. All the data we create—all of our emails, tweets, selfies, A.I.-generated text and funny cat videos—amounts to an aggregate lifeform. It has goals and needs. It can control our behavior and influence our well-being. And it’s an organism that has evolved right alongside us. This symbiotic relationship with information offers a startling new lens for looking at the world. Data isn’t just something we produce; it’s the reason we exist. This powerful idea has the potential to upend the way we think about our technology, our role as humans, and the fundamental nature of life. The Ascent of Information offers a humbling vision of a universe built of and for information. Scharf explores how our relationship with data will affect our ongoing evolution as a species. Understanding this relationship will be crucial to preventing our data from becoming more of a burden than an asset, and to preserving the possibility of a human future. |
| computer science theory columbia: Complexity Dichotomies for Counting Problems Jin-yi Cai, Chen, Xi, 2017 Complexity theory aims to understand and classify computational problems, especially decision problems, according to their inherent complexity. This book uses new techniques to expand the theory for use with counting problems. The authors present dichotomy classifications for broad classes of counting problems in the realm of P and NP. Classifications are proved for partition functions of spin systems, graph homomorphisms, constraint satisfaction problems, and Holant problems. The book assumes minimal prior knowledge of computational complexity theory, developing proof techniques as needed and gradually increasing the generality and abstraction of the theory. This volume presents the theory on the Boolean domain, and includes a thorough presentation of holographic algorithms, culminating in classifications of computational problems studied in exactly solvable models from statistical mechanics |
| computer science theory columbia: Introduction to Modern Cryptography Jonathan Katz, Yehuda Lindell, 2020-12-21 Now the most used texbook for introductory cryptography courses in both mathematics and computer science, the Third Edition builds upon previous editions by offering several new sections, topics, and exercises. The authors present the core principles of modern cryptography, with emphasis on formal definitions, rigorous proofs of security. |
| computer science theory columbia: Foundations of Computer Science Alfred V. Aho, Jeffrey D. Ullman, 1994-10-15 |
| computer science theory columbia: Complexity Theory, Game Theory, and Economics Tim Roughgarden, 2020-03-02 This monograph comprises a series of ten lectures divided into two parts. Part 1 focuses on the communication and computational complexity of computing an (approximate) Nash equilibrium. Part 2 focuses on applications of computational complexity theory to game theory and economics. |
| computer science theory columbia: Theoretical Neuroscience Peter Dayan, Laurence F. Abbott, 2005-08-12 Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site. |
| computer science theory columbia: Communication Complexity (for Algorithm Designers) Tim Roughgarden, 2016-05-11 This book deals mostly with impossibility results - lower bounds on what can be accomplished by algorithms. However, the perspective is unapologetically that of an algorithm designer. The reader will learn lower bound technology on a need-to-know basis, guided by fundamental algorithmic problems that we care about. |
| computer science theory columbia: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| computer science theory columbia: Computer Science – Theory and Applications Rahul Santhanam, Daniil Musatov, 2021-06-16 This book constitutes the proceedings of the 16th International Computer Science Symposium in Russia, CSR 2021, held in Sochi, Russia, in June/July 2021. The 28 full papers were carefully reviewed and selected from 68 submissions. The papers cover a broad range of topics, such as formal languages and automata theory, geometry and discrete structures; theory and algorithms for application domains and much more. |
| computer science theory columbia: Computer Science Edward K. Blum, Alfred V Aho, 2011-12-02 Computer Science: The Hardware, Software and Heart of It focuses on the deeper aspects of the two recognized subdivisions of Computer Science, Software and Hardware. These subdivisions are shown to be closely interrelated as a result of the stored-program concept. Computer Science: The Hardware, Software and Heart of It includes certain classical theoretical computer science topics such as Unsolvability (e.g. the halting problem) and Undecidability (e.g. Godel’s incompleteness theorem) that treat problems that exist under the Church-Turing thesis of computation. These problem topics explain inherent limits lying at the heart of software, and in effect define boundaries beyond which computer science professionals cannot go beyond. Newer topics such as Cloud Computing are also covered in this book. After a survey of traditional programming languages (e.g. Fortran and C++), a new kind of computer Programming for parallel/distributed computing is presented using the message-passing paradigm which is at the heart of large clusters of computers. This leads to descriptions of current hardware platforms for large-scale computing, such as clusters of as many as one thousand which are the new generation of supercomputers. This also leads to a consideration of future quantum computers and a possible escape from the Church-Turing thesis to a new computation paradigm. The book’s historical context is especially helpful during this, the centenary of Turing's birth. Alan Turing is widely regarded as the father of Computer Science, since many concepts in both the hardware and software of Computer Science can be traced to his pioneering research. Turing was a multi-faceted mathematician-engineer and was able to work on both concrete and abstract levels. This book shows how these two seemingly disparate aspects of Computer Science are intimately related. Further, the book treats the theoretical side of Computer Science as well, which also derives from Turing's research. Computer Science: The Hardware, Software and Heart of It is designed as a professional book for practitioners and researchers working in the related fields of Quantum Computing, Cloud Computing, Computer Networking, as well as non-scientist readers. Advanced-level and undergraduate students concentrating on computer science, engineering and mathematics will also find this book useful. |
| computer science theory columbia: The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2013-11-11 During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. |
| computer science theory columbia: Game Theory, Alive Anna R. Karlin, Yuval Peres, 2017-04-27 We live in a highly connected world with multiple self-interested agents interacting and myriad opportunities for conflict and cooperation. The goal of game theory is to understand these opportunities. This book presents a rigorous introduction to the mathematics of game theory without losing sight of the joy of the subject. This is done by focusing on theoretical highlights (e.g., at least six Nobel Prize winning results are developed from scratch) and by presenting exciting connections of game theory to other fields such as computer science (algorithmic game theory), economics (auctions and matching markets), social choice (voting theory), biology (signaling and evolutionary stability), and learning theory. Both classical topics, such as zero-sum games, and modern topics, such as sponsored search auctions, are covered. Along the way, beautiful mathematical tools used in game theory are introduced, including convexity, fixed-point theorems, and probabilistic arguments. The book is appropriate for a first course in game theory at either the undergraduate or graduate level, whether in mathematics, economics, computer science, or statistics. The importance of game-theoretic thinking transcends the academic setting—for every action we take, we must consider not only its direct effects, but also how it influences the incentives of others. |
| computer science theory columbia: Organization at the Limit William Starbuck, Moshe Farjoun, 2005-09-12 The book offers important insight relevant to Corporate, Government and Global organizations management in general. The internationally recognised authors tackle vital issues in decision making, how organizational risk is managed, how can technological and organizational complexities interact, what are the impediments for effective learning and how large, medium, and small organizations can, and in fact must, increase their resilience. Managers, organizational consultants, expert professionals, and training specialists; particularly those in high risk organizations, may find the issues covered in the book relevant to their daily work and a potential catalyst for thought and action. A timely analysis of the Columbia disaster and the organizational lessons that can be learned from it. Includes contributions from those involved in the Investigation Board report into the incident. Tackles vital issues such as the role of time pressures and goal conflict in decision making, and the impediments for effective learning. Examines how organizational risk is managed and how technological and organizational complexities interact. Assesses how large, medium, and small organizations can, and in fact must, increase their resilience. Questions our eagerness to embrace new technologies, yet reluctance to accept the risks of innovation. Offers a step by step understanding of the complex factors that led to disaster. |
| computer science theory columbia: Fundamentals of Speaker Recognition Homayoon Beigi, 2011-12-09 An emerging technology, Speaker Recognition is becoming well-known for providing voice authentication over the telephone for helpdesks, call centres and other enterprise businesses for business process automation. Fundamentals of Speaker Recognition introduces Speaker Identification, Speaker Verification, Speaker (Audio Event) Classification, Speaker Detection, Speaker Tracking and more. The technical problems are rigorously defined, and a complete picture is made of the relevance of the discussed algorithms and their usage in building a comprehensive Speaker Recognition System. Designed as a textbook with examples and exercises at the end of each chapter, Fundamentals of Speaker Recognition is suitable for advanced-level students in computer science and engineering, concentrating on biometrics, speech recognition, pattern recognition, signal processing and, specifically, speaker recognition. It is also a valuable reference for developers of commercial technology and for speech scientists. Please click on the link under Additional Information to view supplemental information including the Table of Contents and Index. |
| computer science theory columbia: Lower Bounds in Communication Complexity Troy Lee, Adi Shraibman, 2009 The communication complexity of a function f(x, y) measures the number of bits that two players, one who knows x and the other who knows y, must exchange to determine the value f(x, y). Communication complexity is a fundamental measure of complexity of functions. Lower bounds on this measure lead to lower bounds on many other measures of computational complexity. This monograph surveys lower bounds in the field of communication complexity. Our focus is on lower bounds that work by first representing the communication complexity measure in Euclidean space. That is to say, the first step in these lower bound techniques is to find a geometric complexity measure, such as rank or trace norm, that serves as a lower bound to the underlying communication complexity measure. Lower bounds on this geometric complexity measure are then found using algebraic and geometric tools. |
| computer science theory columbia: Hidden Dimensions B. Alan Wallace, 2010-02-22 B. Alan Wallace introduces a natural theory of human consciousness that has its roots in contemporary physics and Buddhism. Wallace's special theory of ontological relativity suggests that mental phenomena are conditioned by the brain, but do not emerge from it. Rather, the entire natural world of mind and matter, subjects and objects, arises from a unitary dimension of reality. Wallace employs the Buddhist meditative practice of samatha to test his hypothesis, creating a kind of telescope to examine the space of the mind. He then proposes a more general theory in which the participatory nature of reality is envisioned as a self-excited circuit.In comparing these ideas to the Buddhist theory known as the Middle Way philosophy, Wallace explores further aspects of his general theory of ontological relativity, which can be investigated through vipasyana, or insight, meditation. He then focuses on the theme of symmetry in quantum cosmology and the problem of frozen time, relating these issues to the theory and practices of the Great Perfection school of Tibetan Buddhism. He concludes with a discussion of complementarity as it relates to science and religion. |
| computer science theory columbia: How Much Inequality Is Fair? Venkat Venkatasubramanian, 2017-08-08 Many in the United States feel that the nation’s current level of economic inequality is unfair and that capitalism is not working for 90% of the population. Yet some inequality is inevitable. The question is: What level of inequality is fair? Mainstream economics has offered little guidance on fairness and the ideal distribution of income. Political philosophy, meanwhile, has much to say about fairness yet relies on qualitative theories that cannot be verified by empirical data. To address inequality, we need to know what the goal is—and for this, we need a quantitative, testable theory of fairness for free-market capitalism. How Much Inequality Is Fair? synthesizes concepts from economics, political philosophy, game theory, information theory, statistical mechanics, and systems engineering into a mathematical framework for a fair free-market society. The key to this framework is the insight that maximizing fairness means maximizing entropy, which makes it possible to determine the fairest possible level of pay inequality. The framework therefore provides a moral justification for capitalism in mathematical terms. Venkat Venkatasubramanian also compares his theory’s predictions to actual inequality data from various countries—showing, for instance, that Scandinavia has near-ideal fairness, while the United States is markedly unfair—and discusses the theory’s implications for tax policy, social programs, and executive compensation. |
| computer science theory columbia: Information Eric Hayot, Samuel Frederick, Jonathan E. Abel, Michele Kennerly, 2021-01-05 Bringing together essays by prominent critics, Information: Keywords highlights the humanistic nature of information practices and concepts by thinking through key terms. It describes and anticipates directions for how the humanities can contribute to our understanding of information from a range of theoretical, historical, and global perspectives. |
| computer science theory columbia: Introduction To Algorithms Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, Clifford Stein, 2001 An extensively revised edition of a mathematically rigorous yet accessible introduction to algorithms. |
| computer science theory columbia: Six Degrees: The Science of a Connected Age Duncan J. Watts, 2004-01-27 Watts, one of the principal architects of network theory, sets out to explain the innovative research that he and other scientists are spearheading to create a blueprint of this connected planet. |
| computer science theory columbia: Peterson's Graduate Programs in Computer Science & Information Technology, Electrical & Computer Engineering, and Energy & Power Engineering 2011 Peterson's, 2011-05-01 Peterson's Graduate Programs in Computer Science & Information Technology, Electrical & Computer Engineering, and Energy & Power Engineering contains a wealth of information on colleges and universities that offer graduate work these exciting fields. The profiled institutions include those in the United States, Canada and abroad that are accredited by U.S. accrediting bodies. Up-to-date data, collected through Peterson's Annual Survey of Graduate and Professional Institutions, provides valuable information on degree offerings, professional accreditation, jointly offered degrees, part-time and evening/weekend programs, postbaccalaureate distance degrees, faculty, students, degree requirements, entrance requirements, expenses, financial support, faculty research, and unit head and application contact information. Readers will find helpful links to in-depth descriptions that offer additional detailed information about a specific program or department, faculty members and their research, and much more. In addition, there are valuable articles on financial assistance, the graduate admissions process, advice for international and minority students, and facts about accreditation, with a current list of accrediting agencies. |
| computer science theory columbia: Graph Theory and Its Applications Jonathan L. Gross, Jay Yellen, Mark Anderson, 2018-11-05 Graph Theory and Its Applications, Third Edition is the latest edition of the international, bestselling textbook for undergraduate courses in graph theory, yet it is expansive enough to be used for graduate courses as well. The textbook takes a comprehensive, accessible approach to graph theory, integrating careful exposition of classical developments with emerging methods, models, and practical needs. The authors’ unparalleled treatment is an ideal text for a two-semester course and a variety of one-semester classes, from an introductory one-semester course to courses slanted toward classical graph theory, operations research, data structures and algorithms, or algebra and topology. Features of the Third Edition Expanded coverage on several topics (e.g., applications of graph coloring and tree-decompositions) Provides better coverage of algorithms and algebraic and topological graph theory than any other text Incorporates several levels of carefully designed exercises that promote student retention and develop and sharpen problem-solving skills Includes supplementary exercises to develop problem-solving skills, solutions and hints, and a detailed appendix, which reviews the textbook’s topics About the Authors Jonathan L. Gross is a professor of computer science at Columbia University. His research interests include topology and graph theory. Jay Yellen is a professor of mathematics at Rollins College. His current areas of research include graph theory, combinatorics, and algorithms. Mark Anderson is also a mathematics professor at Rollins College. His research interest in graph theory centers on the topological or algebraic side. |
| computer science theory columbia: Issues in Computer Science and Theory: 2013 Edition , 2013-05-01 Issues in Computer Science and Theory / 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Soft Computing. The editors have built Issues in Computer Science and Theory: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Soft Computing in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Computer Science and Theory: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/. |
| computer science theory columbia: Nearest-neighbor Methods in Learning and Vision Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk, 2005 This text presents theoretical and practical discussions of nearest neighbour (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. |
| computer science theory columbia: The Cultural Logic of Computation David Golumbia, 2009-04-30 Advocates of computers make sweeping claims for their inherently transformative power: new and different from previous technologies, they are sure to resolve many of our existing social problems, and perhaps even to cause a positive political revolution. In The Cultural Logic of Computation, David Golumbia, who worked as a software designer for more than ten years, confronts this orthodoxy, arguing instead that computers are cultural “all the way down”—that there is no part of the apparent technological transformation that is not shaped by historical and cultural processes, or that escapes existing cultural politics. From the perspective of transnational corporations and governments, computers benefit existing power much more fully than they provide means to distribute or contest it. Despite this, our thinking about computers has developed into a nearly invisible ideology Golumbia dubs “computationalism”—an ideology that informs our thinking not just about computers, but about economic and social trends as sweeping as globalization. Driven by a programmer’s knowledge of computers as well as by a deep engagement with contemporary literary and cultural studies and poststructuralist theory, The Cultural Logic of Computation provides a needed corrective to the uncritical enthusiasm for computers common today in many parts of our culture. |
| computer science theory columbia: The Nature of Statistical Learning Theory Vladimir Vapnik, 2013-06-29 The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. |
| computer science theory columbia: Why Evolution is True Jerry A. Coyne, 2010-01-14 For all the discussion in the media about creationism and 'Intelligent Design', virtually nothing has been said about the evidence in question - the evidence for evolution by natural selection. Yet, as this succinct and important book shows, that evidence is vast, varied, and magnificent, and drawn from many disparate fields of science. The very latest research is uncovering a stream of evidence revealing evolution in action - from the actual observation of a species splitting into two, to new fossil discoveries, to the deciphering of the evidence stored in our genome. Why Evolution is True weaves together the many threads of modern work in genetics, palaeontology, geology, molecular biology, anatomy, and development to demonstrate the 'indelible stamp' of the processes first proposed by Darwin. It is a crisp, lucid, and accessible statement that will leave no one with an open mind in any doubt about the truth of evolution. |
| computer science theory columbia: Algorithmic Thinking Daniel Zingaro, 2020-12-15 A hands-on, problem-based introduction to building algorithms and data structures to solve problems with a computer. Algorithmic Thinking will teach you how to solve challenging programming problems and design your own algorithms. Daniel Zingaro, a master teacher, draws his examples from world-class programming competitions like USACO and IOI. You'll learn how to classify problems, choose data structures, and identify appropriate algorithms. You'll also learn how your choice of data structure, whether a hash table, heap, or tree, can affect runtime and speed up your algorithms; and how to adopt powerful strategies like recursion, dynamic programming, and binary search to solve challenging problems. Line-by-line breakdowns of the code will teach you how to use algorithms and data structures like: The breadth-first search algorithm to find the optimal way to play a board game or find the best way to translate a book Dijkstra's algorithm to determine how many mice can exit a maze or the number of fastest routes between two locations The union-find data structure to answer questions about connections in a social network or determine who are friends or enemies The heap data structure to determine the amount of money given away in a promotion The hash-table data structure to determine whether snowflakes are unique or identify compound words in a dictionary NOTE: Each problem in this book is available on a programming-judge website. You'll find the site's URL and problem ID in the description. What's better than a free correctness check? |
| computer science theory columbia: Invitation to Computer Science G. Michael Schneider, Judith L. Gersting, 2006 This new edition of Invitation to Computer Science follows the breadth-first guidelines recommended by CC2001 to teach computer science topics from the ground up. The authors begin by showing that computer science is the study of algorithms, the central theme of the book, then move up the next five levels of the hierarchy: hardware, virtual machine, software, applications, and ethics. Utilizing rich pedagogy and a consistently engaging writing style, Schneider and Gersting provide students with a solid grounding in theoretical concepts, as well as important applications of computing and information technology. A laboratory manual and accompanying software is available as an optional bundle with this text. |
| computer science theory columbia: Precision Journalism Philip Meyer, 2002-02-25 Philip Meyer's work in precision journalism established a new and ongoing trend-the use by reporters of social science research techniques to increase the depth and accuracy of major stories. In this fully updated, fourth edition of the classic Precision Journalism (known as The New Precision Journalism in its third edition), Meyer shows journalists and students of journalism how to use new technology to analyze data and provide more precise information in easier-to-understand forms. New to this edition are an overview of the use of theory and science in journalism; game theory applications; introductions to lurking variables and multiple and logistic regression; and developments in election surveys. Key topics retained and updated include elements of data analysis; the use of statistics, computers, surveys, and experiments; database applications; and the politics of precision journalism. This accessible book is an important resource for working journalists and an indispensable text for all journalism majors. |
| computer science theory columbia: Integrating Computer Science Across the Core Tom Liam Lynch, Gerald Ardito, Pam Amendola, 2020-03-24 Integrating Computer Science Across the Core is a guide to systematizing computer science and computational thinking practices in your school. While most books explain how to teach computer science as a stand-alone discipline, this innovative approach will help you leverage your existing curriculum to deepen and expand students’ learning experiences in all content areas. Effective, equitable, and sustainable, this blueprint provides principals, curriculum directors, directors of technology, and other members of your school or district leadership team with suggested organizational structures, tips for professional learning, and key resources like planning instruments. |
| computer science theory columbia: Theory of Cryptography Eyal Kushilevitz, Tal Malkin, 2015-12-21 The two-volume set LNCS 9562 and LNCS 9563 constitutes the refereedproceedings of the 13th International Conference on Theory ofCryptography, TCC 2016, held in Tel Aviv, Israel, in January 2016. The 45 revised full papers presented were carefully reviewed andselected from 112 submissions. The papers are organized in topicalsections on obfuscation, differential privacy, LWR and LPN, public key encryption, signatures, and VRF, complexity of cryptographic primitives, multiparty computation, zero knowledge and PCP, oblivious RAM, ABE and IBE, and codes and interactive proofs. The volume also includes an invited talk on cryptographic assumptions. |
| computer science theory columbia: Discrete Mathematics and Its Applications Kenneth H. Rosen, 2003 Discrete Mathematics and its Applications is a focused introduction to the primary themes in a discrete mathematics course, as introduced through extensive applications, expansive discussion, and detailed exercise sets. These themes include mathematical reasoning, combinatorial analysis, discrete structures, algorithmic thinking, and enhanced problem-solving skills through modeling. Its intent is to demonstrate the relevance and practicality of discrete mathematics to all students. The Fifth Edition includes a more thorough and linear presentation of logic, proof types and proof writing, and mathematical reasoning. This enhanced coverage will provide students with a solid understanding of the material as it relates to their immediate field of study and other relevant subjects. The inclusion of applications and examples to key topics has been significantly addressed to add clarity to every subject. True to the Fourth Edition, the text-specific web site supplements the subject matter in meaningful ways, offering additional material for students and instructors. Discrete math is an active subject with new discoveries made every year. The continual growth and updates to the web site reflect the active nature of the topics being discussed. The book is appropriate for a one- or two-term introductory discrete mathematics course to be taken by students in a wide variety of majors, including computer science, mathematics, and engineering. College Algebra is the only explicit prerequisite. |
| computer science theory columbia: Analysis of Boolean Functions Ryan O'Donnell, 2014-06-05 This graduate-level text gives a thorough overview of the analysis of Boolean functions, beginning with the most basic definitions and proceeding to advanced topics. |
| computer science theory columbia: Combinatorial Methods with Computer Applications Jonathan L. Gross, 2016-04-19 This combinatorics text provides in-depth coverage of recurrences, generating functions, partitions, and permutations, along with some of the most interesting graph and network topics, design constructions, and finite geometries. It presents the computer and software algorithms in pseudo-code and incorporates definitions, theorems, proofs, examples, and nearly 300 illustrations as pedagogical elements of the exposition. Numerous problems, solutions, and hints reinforce basic skills and assist with creative problem solving. The author also offers a website with extensive graph theory informational resources as well as a computational engine to help with calculations for some of the exercises. |
| computer science theory columbia: Probabilistic Machine Learning Kevin P. Murphy, 2022-03-01 A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach. |
| computer science theory columbia: Theory of Cryptography Shai Halevi, Tal Rabin, 2006-03-01 This book constitutes the refereed proceedings of the Third Theory of Cryptography Conference, TCC 2006, held in March 2006. The 31 revised full papers presented were carefully reviewed and selected from 91 submissions. The papers are organized in topical sections on zero-knowledge, primitives, assumptions and models, the bounded-retrieval model, privacy, secret sharing and multi-party computation, universally-composible security, one-way functions and friends, and pseudo-random functions and encryption. |
| computer science theory columbia: Columbia Crew Survival Investigation Report Nasa, 2009 NASA commissioned the Columbia Accident Investigation Board (CAIB) to conduct a thorough review of both the technical and the organizational causes of the loss of the Space Shuttle Columbia and her crew on February 1, 2003. The accident investigation that followed determined that a large piece of insulating foam from Columbia's external tank (ET) had come off during ascent and struck the leading edge of the left wing, causing critical damage. The damage was undetected during the mission. The Columbia accident was not survivable. After the Columbia Accident Investigation Board (CAIB) investigation regarding the cause of the accident was completed, further consideration produced the question of whether there were lessons to be learned about how to improve crew survival in the future. This investigation was performed with the belief that a comprehensive, respectful investigation could provide knowledge that can protect future crews in the worldwide community of human space flight. Additionally, in the course of the investigation, several areas of research were identified that could improve our understanding of both nominal space flight and future spacecraft accidents. This report is the first comprehensive, publicly available accident investigation report addressing crew survival for a human spacecraft mishap, and it provides key information for future crew survival investigations. The results of this investigation are intended to add meaning to the sacrifice of the crew's lives by making space flight safer for all future generations. |
Computer - Wikipedia
A computer is a machine that can be programmed to automatically carry out sequences of arithmetic or logical operations (computation). Modern digital electronic computers can …
Computer | Definition, History, Operating Systems, & Facts
A computer is a programmable device for processing, storing, and displaying information. Learn more in this article about modern digital electronic computers and their design, constituent …
What is a Computer?
Feb 6, 2025 · What is a Computer? A computer is a programmable device that stores, retrieves, and processes data. The term "computer" was originally given to humans (human computers) …
Micro Center - Computer & Electronics Retailer - Shop Now
Shop Micro Center for electronics, PCs, laptops, Apple products, and much more. Enjoy in-store pickup, top deals, and expert same-day tech support.
What is a Computer? - GeeksforGeeks
Apr 7, 2025 · A computer is an electronic device that processes, stores, and executes instructions to perform tasks. It includes key components such as the CPU (Central Processing Unit), RAM …
Computer Basics: What is a Computer? - GCFGlobal.org
What is a computer? A computer is an electronic device that manipulates information, or data. It has the ability to store, retrieve, and process data. You may already know that you can use a …
What is a Computer? (Definition & Meaning) - Webopedia
Oct 9, 2024 · A computer is a programmable machine that responds to specific instructions and uses hardware and software to perform tasks. Different types of computers, including …
Computer - Simple English Wikipedia, the free encyclopedia
A computer is a machine that uses electronics to input, process, store, and output data. Data is information such as numbers, words, and lists. Input of data means to read information from a …
Laptop & Desktop Computers - Staples
Buy the computer that fits your exact needs. Choose from laptops, desktops PCs, notebooks, and accessories. Invest in a quality computer for work or personal use.
What is Computer? Definition, Characteristics and Classification
Aug 7, 2024 · A computer is an electronic device wherein we need to input raw data to be processed with a set of programs to produce a desirable output. Computers have the ability to …
Computer - Wikipedia
A computer is a machine that can be programmed to automatically carry out sequences of arithmetic or logical operations (computation). Modern digital electronic computers can …
Computer | Definition, History, Operating Systems, & Facts
A computer is a programmable device for processing, storing, and displaying information. Learn more in this article about modern digital electronic computers and their design, constituent …
What is a Computer?
Feb 6, 2025 · What is a Computer? A computer is a programmable device that stores, retrieves, and processes data. The term "computer" was originally given to humans (human computers) …
Micro Center - Computer & Electronics Retailer - Shop Now
Shop Micro Center for electronics, PCs, laptops, Apple products, and much more. Enjoy in-store pickup, top deals, and expert same-day tech support.
What is a Computer? - GeeksforGeeks
Apr 7, 2025 · A computer is an electronic device that processes, stores, and executes instructions to perform tasks. It includes key components such as the CPU (Central Processing Unit), RAM …
Computer Basics: What is a Computer? - GCFGlobal.org
What is a computer? A computer is an electronic device that manipulates information, or data. It has the ability to store, retrieve, and process data. You may already know that you can use a …
What is a Computer? (Definition & Meaning) - Webopedia
Oct 9, 2024 · A computer is a programmable machine that responds to specific instructions and uses hardware and software to perform tasks. Different types of computers, including …
Computer - Simple English Wikipedia, the free encyclopedia
A computer is a machine that uses electronics to input, process, store, and output data. Data is information such as numbers, words, and lists. Input of data means to read information from a …
Laptop & Desktop Computers - Staples
Buy the computer that fits your exact needs. Choose from laptops, desktops PCs, notebooks, and accessories. Invest in a quality computer for work or personal use.
What is Computer? Definition, Characteristics and Classification
Aug 7, 2024 · A computer is an electronic device wherein we need to input raw data to be processed with a set of programs to produce a desirable output. Computers have the ability to …
